This study investigates the utility of speech signals for AI-based depression
screening across varied interaction scenarios, including psychiatric
interviews, chatbot conversations, and text readings. Participants includes
depressed patients recruited from the outpatient clinics of Peking University
Sixth Hospital and control group members from the community, all diagnosed by
psychiatrists following standardized diagnostic protocols. We extracted
acoustic and deep speech features from each participant's segmented recordings.
Classifications were made using neural networks or SVMs, with aggregated clip
outcomes determining final assessments. Our analysis across interaction
scenarios, speech processing techniques, and feature types confirms speech as a
crucial marker for depression screening. Specifically, human-computer
interaction matches clinical interview efficacy, surpassing reading tasks.
Segment duration and quantity significantly affect model performance, with deep
speech features substantially outperforming traditional acoustic features.

通过对不同交互场景中的语音信号进行研究，包括精神科面谈、聊天机器人对话和文字朗读，本研究探究了语音信号在基于人工智能的抑郁症筛查中的实用性。利用从每个参与者的分割录音中提取的声学和深度语音特征，使用神经网络或支持向量机进行分类，并通过聚合的音频片段结果确定最终评估。分析结果表明，语音在抑郁症筛查中是一个关键的标志物，人机交互的效果与临床面谈相匹配，甚至优于阅读任务。片段时长和数量对模型性能有显著影响，而深度语音特征在表现上明显优于传统声学特征。